With the advent of in-line sensing technologies, it has become more and more common that color marking systems are able to gather information about image quality (IQ) defects and utilize that information, for example, to improve their image quality metric. The success of these technologies relies heavily on the timely availability of the information of image quality defects. A full characterization of the entire color space for a given image quality metric, such as mottle, graininess, banding, and temporal color variations requires a large number of printed patches. For many applications, it can be prohibitive to print and measure all colors of interest to build a comprehensive image quality metric database over a target device's entire color gamut.
Moreover, image marking devices change over time due to operating conditions including wear and tear. This change in the characteristics of an image marking device over time is often referred to as “drift”. If entries in the image quality database remain static, these will not properly track the drift of the image marking device. Often times, the image quality database must be updated by subsequently printing and measuring of large numbers of color patches. This repeated operation is time consuming in effort, paper, and other resources.
Accordingly, what is needed in this art are increasingly sophisticated systems and methods for determining image quality metric for any color of interest based upon measurements obtained for a small number of colors and accommodate the drift of the image marking device.